DocuRAG

# pyLLMSearch - Advanced RAG [Documentation](https://llm-search.readthedocs.io/en/latest/) The purpose of this package is to offer an advanced question-answering (RAG) system with a simple YAML-based configuration that enables interaction with a collection of local documents. Special attention is given to improvements in various components of the system **in addition to basic LLM-based RAGs** - better document parsing, hybrid search, HyDE, chat history, deep linking, re-ranking, the ability to customize embeddings, and more. The package is designed to work with custom Large Language Models (LLMs) – whether from OpenAI or installed locally. Interaction with the package is supported through the built-in frontend, or by exposing an MCP server, allowing clients like Cursor, Windsurf or VSCode GH Copilot to interact with the RAG system. ## Features * Fast parsing and embedding of medium size document bases (tested on up to few gigabytes of markdown and pdfs) * Incremental updates for new documents, without a need to re-index the entire document base. * Supported document formats * Build-in parsers: * `.md` - Divides files based on logical components such as headings, subheadings, and code blocks. Supports additional features like cleaning image links, adding custom metadata, and more. * `.pdf` - MuPDF-based parser. * `.docx` - custom parser, supports nested tables. * Other common formats are supported by `Unstructured` pre-processor: * List of formats see [here](https://unstructured-io.github.io/unstructured/core/partition.html). * FastAPI based API + MCP server, allowing communicating with RAG via any mcp client, including VSCode/Windsurf/Cursor and others. * Deep linking into document sections - jump to an individual PDF page or a header in a markdown file. * Allows interaction with embedded documents, internally supporting the following models and methods (including locally hosted): * OpenAI compatible models and APIs. * HuggingFace models. * Interoperability with LiteLLM + Ollama via OpenAI API, supporting hundreds of different models (see [Model configuration for LiteLLM](sample_templates/llm/litellm.yaml)) * SSE MCP Server enabling interface with popular MCP clients. * Hybrid search and Reranking * Dense embeddings from a folder of documents and stores them in a vector database ([ChromaDB](https://github.com/chroma-core/chroma)). * The following embedding models are supported: * Hugging Face embeddings. * Sentence-transformers-based models. * Instructor-based models. * OpenAI embeddings. * Sparse embeddings using SPLADE (https://github.com/naver/splade) to enable hybrid search (sparse + dense). * Supports the "Retrieve and Re-rank" strategy for semantic search, see [here](https://www.sbert.net/examples/applications/retrieve_rerank/README.html). * Besides the originally `ms-marco-MiniLM` cross-encoder, more modern `bge-reranker-v2-m3` and `zerank-2` is supported. * Support for table parsing via open-source gmft (https://github.com/conjuncts/gmft) or Azure Document Intelligence. * Optional support for image parsing using Gemini API. * Supports HyDE (Hypothetical Document Embeddings) - see [here](https://arxiv.org/pdf/2212.10496.pdf). * WARNING: Enabling HyDE (via config OR webapp) can significantly alter the quality of the results. Please make sure to read the paper before enabling. * From my own experiments, enabling HyDE significantly boosts quality of the output on a topics where user can't formulate the quesiton using domain specific language of the topic - e.g. when learning new topics. * Support for multi-querying, inspired by `RAG Fusion` - https://towardsdatascience.com/forget-rag-the-future-is-rag-fusion-1147298d8ad1 * When multi-querying is turned on (either config or webapp), the original query will be replaced by 3 variants of the same query, allowing to bridge the gap in the terminology and "offer different angles or perspectives" according to the article. * Supprts optional chat history with question contextualization * Other features * Simple web interfaces. * Ability to save responses to an offline database for future analysis. ## Demo  ## Documentation [Browse Documentation](https://llm-search.readthedocs.io/en/latest/)